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Toward Robots That Reason: Logic, Probability & Causal Laws: Synthesis Lectures on Artificial Intelligence and Machine Learning

Autor Vaishak Belle
en Limba Engleză Paperback – 22 feb 2024
This book discusses the two fundamental elements that underline the science and design of artificial intelligence (AI) systems: the learning and acquisition of knowledge from observational data, and the reasoning of that knowledge together with whatever information is available about the application at hand. It then presents a mathematical treatment of the core issues that arise when unifying first-order logic and probability, especially in the presence of dynamics, including physical actions, sensing actions and their effects. A model for expressing causal laws describing dynamics is also considered, along with computational ideas for reasoning with such laws over probabilistic logical knowledge.
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Specificații

ISBN-13: 9783031210051
ISBN-10: 3031210050
Pagini: 190
Ilustrații: XIII, 190 p. 27 illus., 14 illus. in color.
Dimensiuni: 168 x 240 mm
Ediția:2023
Editura: Springer International Publishing
Colecția Springer
Seria Synthesis Lectures on Artificial Intelligence and Machine Learning

Locul publicării:Cham, Switzerland

Cuprins

Preface.- Acknowledgments.- Introduction.- Representation Matters.- From Predicate Calculus to the Situation Calculus.- Knowledge.- Probabilistic Beliefs.- Continuous Distributions.- Localization.- Regression & Progression.- Programs.- A Modal Reconstruction.- Conclusions.

Notă biografică

Vaishak Belle, Ph.D., is a Chancellor’s Fellow and Reader at The University of Edinburgh School of Informatics. He is also an Alan Turing Institute Faculty Fellow, a Royal Society University Research Fellow, and a member of the Royal Society of Edinburgh’s Young Academy of Scotland. Dr. Belle directs a research lab on artificial intelligence at The University of Edinburgh, specializing in the unification of symbolic logic and machine learning. He has co-authored over 50 scientific articles on AI, and has won several best paper awards.

Textul de pe ultima copertă

This book discusses the two fundamental elements that underline the science and design of artificial intelligence (AI) systems: the learning and acquisition of knowledge from observational data, and the reasoning of that knowledge together with whatever information is available about the application at hand. It then presents a mathematical treatment of the core issues that arise when unifying first-order logic and probability, especially in the presence of dynamics, including physical actions, sensing actions and their effects. A model for expressing causal laws describing dynamics is also considered, along with computational ideas for reasoning with such laws over probabilistic logical knowledge. 

Caracteristici

Explains the need for integrating logic and probability in AI systems and the challenges that arise in doing so Presents a model for capturing causal laws that describe dynamics and computational reasoning ideas Includes both high-level ideas and detailed exercises that employ technical applications